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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Real-time monitoring and risk management in geothermal energy production: ensuring safe and efficient operations

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  • Real-time monitoring and risk management in geothermal energy production: ensuring safe and efficient operations

Kate Aigbaifie Iwe 1, Gideon Oluseyi Daramola 2, Daniel Edet Isong 1, *, Mercy Odochi Agho 1 and Michael Osinakachukwu Ezeh 3

1 Independent Researcher, Port Harcourt, Nigeria.
2 Independent Researcher, Lagos, Nigeria.
3 Independent Researcher Abuja, Nigeria.

Review Article
 
International Journal of Science and Research Archive, 2023, 08(01), 995–1011.
Article DOI: 10.30574/ijsra.2023.8.1.0074
DOI url: https://doi.org/10.30574/ijsra.2023.8.1.0074

Received on 13 December 2022; revised on 12 February 2023; accepted on 16 February 2023

Real-time monitoring and risk management are critical in geothermal energy production to ensure operational safety, efficiency, and environmental sustainability. As geothermal systems involve high-temperature fluids and complex subsurface dynamics, their development and operation present inherent risks, including reservoir depletion, induced seismicity, equipment failure, and environmental impact. This study examines the integration of advanced technologies for real-time monitoring and predictive risk management in geothermal energy production, emphasizing the role of data analytics, machine learning, and Internet of Things (IoT) devices. Key components of real-time monitoring include sensors for temperature, pressure, and flow rate measurements, as well as geophysical monitoring tools for subsurface imaging and seismic activity detection. These technologies enable operators to gain a comprehensive understanding of reservoir behavior and system performance, facilitating proactive responses to anomalies. Moreover, the integration of machine learning algorithms enhances predictive maintenance and decision-making processes by analyzing historical and real-time data to forecast potential risks and optimize operations. Risk management strategies in geothermal energy focus on identifying, assessing, and mitigating hazards through continuous monitoring and modeling. Automated control systems and IoT-enabled communication networks further enable swift adjustments to operational parameters, reducing the likelihood of equipment damage or environmental incidents. Regulatory frameworks and best practices for risk mitigation, such as the implementation of environmental safeguards and adherence to safety standards, are also discussed. Case studies from global geothermal projects illustrate the effectiveness of real-time monitoring systems in minimizing downtime and improving operational efficiency. These examples highlight the need for robust data integration and cross-disciplinary collaboration to address the challenges of geothermal production. In conclusion, the application of real-time monitoring and advanced risk management techniques enhances the safety, reliability, and sustainability of geothermal energy systems. The findings underscore the importance of technological innovation and comprehensive risk assessment in supporting the global transition to clean and renewable energy sources.
 

Real-Time Monitoring; Geothermal Energy; Risk Management; Iot; Machine Learning; Predictive Maintenance; Induced Seismicity; Environmental Sustainability; Renewable Energy; Operational Safety

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2023-0074.pdf

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Kate Aigbaifie Iwe, Gideon Oluseyi Daramola, Daniel Edet Isong, Mercy Odochi Agho and Michael Osinakachukwu Ezeh. Real-time monitoring and risk management in geothermal energy production: ensuring safe and efficient operations. International Journal of Science and Research Archive, 2023, 08(01), 995–1011. Article DOI: https://doi.org/10.30574/ijsra.2023.8.1.0074

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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